{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>女800米跑</th>\n",
       "      <th>女800米跑分数</th>\n",
       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>85</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>85</td>\n",
       "      <td>3775</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
       "      <td>10</td>\n",
       "      <td>148</td>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
       "      <td>64</td>\n",
       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
       "      <td>50.700001</td>\n",
       "      <td>19.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
       "      <td>22.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.51</td>\n",
       "      <td>78</td>\n",
       "      <td>9.60</td>\n",
       "      <td>68</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>2255</td>\n",
       "      <td>70</td>\n",
       "      <td>158.0</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>19.629999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.00</td>\n",
       "      <td>76</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>13</td>\n",
       "      <td>72</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>2937</td>\n",
       "      <td>85</td>\n",
       "      <td>161.0</td>\n",
       "      <td>55.700001</td>\n",
       "      <td>21.490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>3.45</td>\n",
       "      <td>80</td>\n",
       "      <td>10.18</td>\n",
       "      <td>64</td>\n",
       "      <td>152</td>\n",
       "      <td>62</td>\n",
       "      <td>15</td>\n",
       "      <td>76</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "      <td>2592</td>\n",
       "      <td>76</td>\n",
       "      <td>165.0</td>\n",
       "      <td>48.599998</td>\n",
       "      <td>17.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.01</td>\n",
       "      <td>74</td>\n",
       "      <td>9.67</td>\n",
       "      <td>68</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>41</td>\n",
       "      <td>78</td>\n",
       "      <td>1829</td>\n",
       "      <td>60</td>\n",
       "      <td>154.0</td>\n",
       "      <td>43.599998</td>\n",
       "      <td>18.379999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>17</td>\n",
       "      <td>女</td>\n",
       "      <td>4.48</td>\n",
       "      <td>50</td>\n",
       "      <td>9.09</td>\n",
       "      <td>74</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "      <td>68</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>2962</td>\n",
       "      <td>85</td>\n",
       "      <td>162.0</td>\n",
       "      <td>55.299999</td>\n",
       "      <td>21.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>593 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0     1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1     1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2     1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3     1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4     1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "..   .. ..     ...       ...    ...      ...  ...    ...   ...     ...  ...   \n",
       "588  17  女    3.51        78   9.60       68  150     60    24      95   41   \n",
       "589  17  女    4.00        76  10.18       64  150     60    13      72   36   \n",
       "590  17  女    3.45        80  10.18       64  152     62    15      76   35   \n",
       "591  17  女    4.01        74   9.67       68  165     70    10      68   41   \n",
       "592  17  女    4.48        50   9.09       74  180     80    10      68   46   \n",
       "\n",
       "     女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0       85  3775     100  163.0  51.299999  19.309999  \n",
       "1       66  3683     100  163.0  66.599998  25.070000  \n",
       "2       76  3331     100  157.0  60.000000  24.340000  \n",
       "3       85  3701     100  160.0  50.700001  19.799999  \n",
       "4       70  3592     100  167.0  63.900002  22.910000  \n",
       "..     ...   ...     ...    ...        ...        ...  \n",
       "588     78  2255      70  158.0  49.000000  19.629999  \n",
       "589     72  2937      85  161.0  55.700001  21.490000  \n",
       "590     72  2592      76  165.0  48.599998  17.850000  \n",
       "591     78  1829      60  154.0  43.599998  18.379999  \n",
       "592     85  2962      85  162.0  55.299999  21.070000  \n",
       "\n",
       "[593 rows x 17 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt  # 画图的画笔\n",
    "\n",
    "data = pd.read_excel(\"./体测分数_女生.xls\")\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00    26\n",
      "1.00     1\n",
      "2.58     1\n",
      "3.03     1\n",
      "3.04     1\n",
      "        ..\n",
      "5.15     1\n",
      "5.17     2\n",
      "5.29     1\n",
      "5.39     1\n",
      "5.48     1\n",
      "Name: 女800米跑, Length: 113, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1=data.女800米跑\n",
    "cond=p1<10\n",
    "p2=p1[cond]\n",
    "p2.sort_values()\n",
    "x=p2.value_counts()\n",
    "d=x.sort_index()\n",
    "print(x.sort_index())\n",
    "\n",
    "_=plt.hist(d,bins = 100,color = 'orange',density=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "80a78411240e314dd53bda626b55ff202cf525fdd1fdf9cfbba660dfec90559a"
  },
  "kernelspec": {
   "display_name": "Python 3.9.5 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
